Entropy analysis of OCT signal for automatic tissue characterization

Yahui Wang, Yi Qiu, Farzana Zaki, Yiqing Xu, Basil Hubbi, Kevin D. Belfield, Xuan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Optical coherence tomography (OCT) signal can provide microscopic characterization of biological tissue and assist clinical decision making in real-time. However, raw OCT data is noisy and complicated. It is challenging to extract information that is directly related to the pathological status of tissue through visual inspection on huge volume of OCT signal streaming from the high speed OCT engine. Therefore, it is critical to discover concise, comprehensible information from massive OCT data through novel strategies for signal analysis. In this study, we perform Shannon entropy analysis on OCT signal for automatic tissue characterization, which can be applied in intraoperative tumor margin delineation for surgical excision of cancer. The principle of this technique is based on the fact that normal tissue is usually more structured with higher entropy value, compared to pathological tissue such as cancer tissue. In this study, we develop high-speed software based on graphic processing units (GPU) for real-time entropy analysis of OCT signal.

Original languageEnglish (US)
Title of host publicationHigh-Speed Biomedical Imaging and Spectroscopy
Subtitle of host publicationToward Big Data Instrumentation and Management
EditorsKeisuke Goda, Kevin K. Tsia
ISBN (Electronic)9781628419542
StatePublished - Jul 18 2016
EventHigh-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management - San Francisco, United States
Duration: Feb 13 2016Feb 14 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherHigh-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management
Country/TerritoryUnited States
CitySan Francisco

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


  • OCT
  • imaging
  • information entropy


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